tf_agents.utils.common.soft_variables_update

Performs a soft/hard update of variables from the source to the target.

tf_agents.utils.common.soft_variables_update(
    source_variables,
    target_variables,
    tau=1.0,
    tau_non_trainable=None,
    sort_variables_by_name=False
)

Note: when using this function with TF DistributionStrategy, the strategy.extended.update call (below) needs to be done in a cross-replica context, i.e. inside a merge_call. Please use the Periodically class above that provides this wrapper for you.

For each variable v_t in target variables and its corresponding variable v_s in source variables, a soft update is: v_t = (1 - tau) * v_t + tau * v_s

When tau is 1.0 (the default), then it does a hard update: v_t = v_s

Args
source_variables	list of source variables.
target_variables	list of target variables.
tau	A float scalar in [0, 1]. When tau is 1.0 (the default), we do a hard update. This is used for trainable variables.
tau_non_trainable	A float scalar in [0, 1] for non_trainable variables. If None, will copy from tau.
sort_variables_by_name	A bool, when True would sort the variables by name before doing the update.

Returns
An operation that updates target variables from source variables.

Raises
ValueError	if tau not in [0, 1].
ValueError	if len(source_variables) != len(target_variables).
ValueError	"Method requires being in cross-replica context, use get_replica_context().merge_call()" if used inside replica context.